20-01-2019 - Python for Data Analysis and Visualization.

I started this course in December. So far, I have completed the Data Analysis section.
That's about 40% of the course, 47 out of the 110 lectures, providing me with 40+ working python examples.
The next 2 sections are going to be big.
3 large Data Analysis projects followed by 6 hours of Machine Learning lectures
I did take a long break over the Christmas period and spent some time concentrating on website work.
I expect to achieve my certificate of completion in February.

I am also thinking of taking DataCamp modules on the topics I will be covering.
Python for Visualisation, Machine Learning and Statistics.
Having achieved 5 Datacamp certificates in May/June of 2018 I would like to add a few more.
DataCamp modules are comprised of programming exercises, around 60 per module.
These will test my understanding,, introduce new elements, provide more working examples.and aid in my revision.

So far, I have been concentrating on data manipulation, mapping and cleaning. The bedrock of Data Analysis.
If you don't have good coherent data you cannot provide good analysis or business intelligence of value.

I really like Jose Portilla as a lecturer. He takes his time and writes in 'simples'. I loves 'simples'.
There are 21 hours of lectures in the course. As a rule of thumb, I allocate 3 hours of study time per hour of lecture.
That would include installation, note taking, code checking, general computer admin, visiting sites of interest and coffee.
He provides Jupiter Notebooks for each lecture, where necessary. Thanks so much!
100,000+ students for this single course is a huge vote of confidence in his teaching ability.
The 60+ hours I spend on this course will be invaluable.